Your privacy, your choice

We use essential cookies to make sure the site can function. We also use optional cookies for advertising, personalisation of content, usage analysis, and social media.

By accepting optional cookies, you consent to the processing of your personal data - including transfers to third parties. Some third parties are outside of the European Economic Area, with varying standards of data protection.

See our privacy policy for more information on the use of your personal data.

for further information and to change your choices.
Skip to main content

Table 6 Diagnostic performances of current machine-learning models

From: Machine learning-based prediction of the necessity for the surgical treatment of distal radius fractures

Model type

Performance indicator

Image only

Image data + Clinical data

Accuracy (%)

89.47

92.98

Sensitivity (%)

86.36

93.28

Specificity (%)

93.75

92.55

Positive predictive value (%)

95.00

94.69

Negative predictive value (%)

83.33

90.62

AUC

0.9751

0.9841

  1. * Descriptive values are presented as percentages or actual values
  2. AUC, area under the curve
Back to article page

Journal of Orthopaedic Surgery and Research

ISSN: 1749-799X